The document discusses the promotion of data competency in various sectors orchestrated by the Digital Analytics Association (DAA), which supports the professionalization of data-driven roles like data scientists. It highlights the significance of integrated industrial analytics as a competitive factor in Industry 4.0, emphasizing the need for organizations to develop relevant competencies and adapt to rapidly changing tools and methods. Key challenges include data quality, system interoperability, and the skill gap in digital professions crucial for Europe's industrial strategy.

1. January
2017
Industrial
Analy.cs
2016
/
2017
Frank
Pörschmann
Frank.poerschmann@digital-­‐analy=cs-­‐associa=on.de

2. 2
• The
focus
is
on
the
promo.on
of
data
competency
in
business,
poli=cs
and
society.
• For
more
than
10
years,
the
Digital
Analy=cs
Associa=on
(DAA),
with
over
5,000
members
worldwide,
has
been
suppor=ng
the
professionaliza=on
of
the
new,
data-­‐driven
professional
images,
such
as
the
digital
analyst
and
the
data
scien=st.
• The
Digital
Analy=cs
Associa=on
e.V.
is
con=nuing
this
commitment
as
an
independent
non-­‐profit
organiza.on
under
its
own
na=onal
leadership.
• The
Digital
Analy=cs
Associa=on
e.V.
supports
ins.tu.ons,
specialists
and
execu.ves
in
the
development
of
professional
and
entrepreneurial
skills
for
the
analysis
of
digital
data
streams.

3. 3
§ Qualifica=on
&
Cer=fica=on
§ Promo=on
of
Young
§ Networking
§ Events
§ Research
&
Development
§ Wegweiser
für
Unternehmen
und
Anwender
§ Career
development
and
support
§ Advisory
services(i.e.
data
rights,
project
management,
tooling)
§ Science
&
Educa=on
||
Promo=on
of
Young
§ Business
&
Governance
§ Soware
Producer
||
Agencies
&
Service
Companies
§ Methods
||
Knowledge
Management
§ Interna=onal
||
Networking
§ Marke=ng,
PR
&
Events
||
Members
§ Legal
Ac.vi.es
Subject
MaMer
Expert
Groups
„Professionaliza-on
of
data-­‐driven
professions
for
data
expert
as
well
as
management.
“

4. Global
representa-ve
decision-­‐maker
study
available
for
download
www.IoT-­‐Analy=cs.de
www.digital-­‐analy=cs-­‐associa=on.de

5. The
concept
of
digital
shadow
applies
to
machines
as
well

6. The
most
expecisve
cost
factor
in
business
s.ll
are
bad
decisions
Signals
Data
Informa=on
Decision
Knowledge
Gathering/management/Distribu=on
Analy=cs
Advanced
Analy=cs
&
Data
Science
-­‐ Learning
systems,
ML,
AI
-­‐ Decision
Support
Systems
-­‐ Automated
decision
support
systems
Conversion
/
Distribu=on
Repor=ng
/
Monitoring
o
Daten-­‐Analy=cs
is
not
new
–
but
different
o 60‘S
&
70‘s:
Opera=onal
Monitoring
o 2017:
Decision
Support
o 2025:
Automated
Decision-­‐making

7. Industrial
Analy-cs...
o is
a
key
success
factor
of
Industrial
Internet
(Industrie
4.0)
o Will
become
a
compe==ve
cri=cal
capability
in
industrial
business
o For
most
of
the
companies
integrated
data
analy=cs
is
a
new
organiza=onal
discipline
(approx.
½
within
one
org.-­‐
unit,
mainly
R&D)
o BUT:
30%
report
about
finalized
projects

8. Development
of
data
competencies
already
on
top-­‐management
agenda
o More
than
half
ini=ated
by
CEO
&
COO
Data
is
not
IT!
o Smallest
responsibility
by
typical
technology
management
CTO/CIO
33,1%

9. Value
expecta-ons
on
industrial
analy-cs
mainly
set
on
growth
instead
of
efficiency
o Expected
benefit
from
analy=cs
mainly
in
growth
and
customer
sa=sfac=on
o Expected
growth
by:
o Extending
exis=ng
products
o Expansion
of
exis=ng
business
models
o New
Business
Models
o Cost-­‐reduc=on
and
efficiency
increase?
Rela=vely
weakly
weighted
despite
numerous
successful
projects
33,1%

10. How
good
are
you
at..?
o Over
half
of
the
companies
are
sa=sfied
with
the
ability
to
access
their
data
o But
about
2/3
of
the
companies
fail
in
genera=ng
sufficiently
relevant
findings
from
the
data
obtained.
-­‐>
But
this
is
the
source
of
future
compe==ve
advantage
33,1%

11. Further:
Cost
and
benefit
structures
are
currently
unbalanced
o Main
costs
of
data
projects
in
IT
&
Technology
disciplines
o Business
benefit
is
generated
by
analy=cs.
Analy=cs
costs
account
for
only
about
25%
of
the
total
costs
o Strategies
for
cost-­‐saving
data
architectures
are
already
relevant
€

12. S-ll
one
Use
Case
seem
to
prevail:
Predic-ve
Maintenance
PdM)

13. Comparison
of
maintenance
approaches
Deep-­‐dive:
Condi=on
Based
Maintenance
vs.
Predic=ve
Maintenance
Copyright
©
2016
by
www.iot-­‐analy=cs.com
All
rights
reserved
13
Condi.on-­‐based
maintenance
Predic.ve
Maintenance
Mobile
Condi-­‐
.on
Monitoring
Online
Condi.on
Monitoring
Sta.s.cs-­‐based
PdM
Stochas.c-­‐based
PdM*
Sensing
Technology
Handheld
Device
Integrated
Sensors
Integrated
Sensors
Integrated
Sensors
Monitoring
Frequency
In
regular
intervals
/
on
demand
Constantly
Constantly
Constantly
Visualiza.on
On
specific
device
Online
/
Mobile
Online
/
Mobile
Online
/
Mobile
IT-­‐Architecture
On-­‐Premise
On-­‐Premise
or
Cloud
On-­‐Premise
or
Cloud
On-­‐Premise
or
Cloud
Real-­‐.me
monitoring
Combina.on
of
data
sources
Analy.cs
Sta=s=cs1
Stochas=cs2
Maintenance
Trigger
If
monitoring
shows
cri=cal
values
If
monitoring
shows
cri=cal
values
When
calculated
health-­‐
score
reaches
cri=cal
value
When
failure
is
predicted
to
occur
a.
b.
a.
b.
*
=
Some=mes
also
referred
to
as
“prognos=cs”
1.
Sta=s=cs
=
Using
sta=s=cal
methods
such
as
SPSS,
regression
2.
Stochas=cs
=
Using
stochas=cal
models
such
as
Bayesian
Networks,
etc.

14. Most
challenging
issues
o Security
remains
strong
obstacle
o However,
the
biggest
hurdles:
o Interoperability
of
systems
o Quality
of
the
data
o Insight
genera=on
by
lack
of
specialists,
skills,
methods,
tools…
33,1%

15. A
rapid
shiK
of
tools
requires
new
skills
and
capabili-es
o The
end
of
spreadsheet
analy=cs
o Rapid
change
of
tools
and
playorms
to
be
experienced
within
5
years
o Importance
of
predic=ve
analy=cs
tools
will
double
o BI
relevance
increases
as
well
33,1%

16. Which
approach
to
use?
Freestyle
or
structured?
o About
2/3
work
on
hypothesis
from
the
begin
o S=ll
1/3
allows
for
gaining
insight
in
their
own
data
33,1%

17. Cri-cal
skill
gap
ahead
Warning
Data
Science
-­‐
Data
Scien=st,
Data
Engineers
-­‐ IT
(Developer,
Architects,
SI,
Infrastructur
(M2M)
-­‐ Agile
PM
-­‐ Industrial
process
know-­‐how
Companies
fail
in
integra=ng
adequate
new
digital
professions
Only
5
years
to
go
un=l
skill
gap
impacts
compe==ve
capability
33,1%

18. Digital
sovereignty
...?
o Promo=on
of
data
competency
among
specialists
and
execu=ves
is
crucial
for
Europe’s
industrial
strategy
o The
German
educa=on
system
in
new
data-­‐driven
professions
is
interna=onally
not
compe==ve.
o Companies
must
take
on-­‐the-­‐job
qualifica=on
and
interna=onalize
skills.
33,1%
HandcraKs
have
many
faces,
so
does
data
art.

19. 19
Contact
Frank
Pörschmann
Crémon
36
20457
Hamburg
0171
–
30
579
20
Frank.Poerschmann@digital-­‐analy=cs-­‐associa=on.de
www.didital-­‐analy=cs-­‐associa=on.de
www.digitalanaly=csassocia=on.org